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Structural Damage Detection Using Multivariate Time Series Analysis
Much research has been focused in the past few decades on data-driven structural health monitoring based on sensor measurements. Modal parameters from system identification are the most widely studied structural state indicators adopted for this purpose; however, recent research has showed that they are not sensitive enough to local damage. In an effort to seek more effective alternatives, univariate autoregressive (AR) modeling on structural response has been investigated in several publications, where model characteristics are used as damage indices. Although these methods are generally successful, they tend to generate false alarms when the environmental conditions are varying because responses from only one location/sensor are considered. To strike a balance between sensitivity and stability, in this paper autoregressive with exogenous input modeling on measurements from several adjacent sensing channels is presented and applied to detect damage in a space truss structure. The damage feature is extracted from the residuals obtained via fitting the baseline model to data from the current structure. Also, damage localization is attempted by examining the estimated mutual information statistic between data from adjacent sensing channels. The damage identification/localization results thus obtained are then compared to those from univariate AR modeling to evaluate their relative pros and cons.
Structural Damage Detection Using Multivariate Time Series Analysis
Much research has been focused in the past few decades on data-driven structural health monitoring based on sensor measurements. Modal parameters from system identification are the most widely studied structural state indicators adopted for this purpose; however, recent research has showed that they are not sensitive enough to local damage. In an effort to seek more effective alternatives, univariate autoregressive (AR) modeling on structural response has been investigated in several publications, where model characteristics are used as damage indices. Although these methods are generally successful, they tend to generate false alarms when the environmental conditions are varying because responses from only one location/sensor are considered. To strike a balance between sensitivity and stability, in this paper autoregressive with exogenous input modeling on measurements from several adjacent sensing channels is presented and applied to detect damage in a space truss structure. The damage feature is extracted from the residuals obtained via fitting the baseline model to data from the current structure. Also, damage localization is attempted by examining the estimated mutual information statistic between data from adjacent sensing channels. The damage identification/localization results thus obtained are then compared to those from univariate AR modeling to evaluate their relative pros and cons.
Structural Damage Detection Using Multivariate Time Series Analysis
Conf.Proceedings of Society
Caicedo, J.M. (editor) / Catbas, F.N. (editor) / Cunha, A. (editor) / Racic, V. (editor) / Reynolds, P. (editor) / Salyards, K. (editor) / Yao, Ruigen (author) / Pakzad, Shamim N. (author)
2012-03-06
10 pages
Article/Chapter (Book)
Electronic Resource
English
Structural Damage Detection Using Multivariate Time Series Analysis
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